模糊环境下ARAS方法中的多层次结构准则

Q3 Computer Science
Maroua Ghram, H. Frikha
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引用次数: 0

摘要

多准则决策辅助(MCDA)的目的是帮助决策者(DM)根据自己的偏好做出合理的决策。事实上,排名方法是当今MCDA领域最常用的方法,因为它们易于DM理解,并且基于现实的假设。层次加性比评价法(ARAS-H)是一种排序方法。它代表了层次结构标准情况下ARAS方法的扩展。然而,大多数情况下,DM无法提供精确的性能值。此后,为了方便他完成任务,他被要求提供语言变量。因此,作者采用了模糊逻辑。事实上,模糊集理论考虑了专家判断的主观性鉴于上述情况,模糊ARAS-H(F-ARAS-H)算法被开发为模糊环境中ARAS-H方法的扩展。为了讨论该算法的可行性,以绿色供应商的选择为例进行了研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multiple Hierarchically Structured Criteria in ARAS Method Under Fuzzy Environment
The aim of multiple criteria decision aiding (MCDA) is to assist decision makers (DMs) to make rational decisions with respect to their preferences. In fact, the ranking approaches are the most used ones nowadays in the MCDA field because they are easy to understand by DMs and they are based on realistic assumptions. The hierarchical additive ratio assessment (ARAS-H) method is a ranking method. It represents an extension of the ARAS method in case of hierarchically structured criteria. However, most often, the DM is unable to provide precise performance values. Henceforth, in order to facilitate the task for him, he is asked to provide linguistic variables. Thus, the authors adopted the fuzzy logic. As a matter of fact, the fuzzy set theory takes into account the subjectivity of experts' ‘judgments.' In the light of the above, the fuzzy ARAS-H (F-ARAS-H) algorithm was developed as an extension of the ARAS-H method in a context of a fuzzy environment. To discuss the feasibility of the proposed algorithm, a case study on the selection of a green supplier was presented.
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来源期刊
International Journal of Fuzzy System Applications
International Journal of Fuzzy System Applications Computer Science-Computer Science (all)
CiteScore
2.40
自引率
0.00%
发文量
65
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